Average Bubble¶

In [1]:
cd '/home/dpirvu/project/paper_prefactor/'

/home/dpirvu/project/paper_prefactor
In [2]:
import os,sys
sys.path.append('/home/dpirvu/project/paper_prefactor/bubble_codes/')
sys.path.remove('/home/dpirvu/DarkPhotonxunWISE/hmvec-master')
print(sys.path)
from plotting import *
from bubble_tools import *
from experiment import *
from celluloid import Camera
from matplotlib.colors import LogNorm

%matplotlib inline

['/home/dpirvu/project/paper_prefactor', '/cm/shared/apps/python/python37/lib/python37.zip', '/cm/shared/apps/python/python37/lib/python3.7', '/cm/shared/apps/python/python37/lib/python3.7/lib-dynload', '', '/home/dpirvu/.local/lib/python3.7/site-packages', '/cm/shared/apps/python/python37/lib/python3.7/site-packages', '/cm/shared/apps/python/python37/lib/python3.7/site-packages/IPython/extensions', '/home/dpirvu/.ipython', '/home/dpirvu/project/paper_prefactor/bubble_codes/']

['/home/dpirvu/project/paper_prefactor', '/cm/shared/apps/python/python37/lib/python37.zip', '/cm/shared/apps/python/python37/lib/python3.7', '/cm/shared/apps/python/python37/lib/python3.7/lib-dynload', '', '/home/dpirvu/.local/lib/python3.7/site-packages', '/cm/shared/apps/python/python37/lib/python3.7/site-packages', '/cm/shared/apps/python/python37/lib/python3.7/site-packages/IPython/extensions', '/home/dpirvu/.ipython', '/home/dpirvu/project/paper_prefactor/bubble_codes/']
In [3]:
case = 'minus'
general = get_general_model(case)
tempList, massq, right_Vmax, V, dV, Vinv, nTimeMAX, minSim, maxSim = general

tmp = 3

general = get_general_model(case)
tempList, massq, right_Vmax, V, dV, Vinv, nTimeMAX, minSim, maxSim = general

maxSim = (1000 if tmp == 0 else 2000 if tmp==1 else 3000 if tmp==2 else 2000)
nTimeMAX = (262144 if tmp!=1 else 131072)

temp, m2, sigmafld = get_model(*general, tmp)
exp_params = [nLat, m2, temp]
print('Experiment', exp_params)

Experiment [2048, 0.7, 0.2]
In [4]:
print('Looking at at T, m2, sigma:', temp, m2, sigmafld)

crit_radList    = np.array(np.linspace(5, 50, 10), dtype='int'); print(crit_radList)
crit_threshList = right_Vmax + np.linspace(0.5, 5, 10) * sigmafld
crit_threshList = np.array([round(ii, 3) for ii in crit_threshList]); print(crit_threshList)

win = 250
plots = False
get_data = True
get_averaged = True

if get_data:
    path = decay_times_file(*exp_params, minSim, maxSim, nTimeMAX)
    if os.path.exists(path):
        decay_times = np.load(path)

        minDecTime = 256
        alltimes   = decay_times[:,1]
        simList2Do = decay_times[alltimes>=minDecTime, 0]
    print('simList2Do', simList2Do)

    all_data = []
    for sim in range(minSim, maxSim):  
        path2RESTsim = rest_sim_location(*exp_params, sim)
        if os.path.exists(path2RESTsim):
            sim, bubble, totbeta = np.load(path2RESTsim, allow_pickle=True)

#             real = np.copy(bubble)
#             real = np.abs(real[0])
#             real = gaussian_filter(real, 1, mode='nearest')
#             nT, nN = np.shape(real)
#             tcen, xcen = find_nucleation_center(real, phieq, 2.7, 30)
#             t, x = np.linspace(-tcen, nT-1-tcen, nT), np.linspace(-xcen, nN-1-xcen, nN)

#             test = bubble[0, tcen, xcen-50:xcen+50]
#             if np.nanmean(test) < 0:
#                 bubble = - bubble

            all_data.append(np.array([sim, bubble]))
            if len(all_data) > 300:
                break
    print('Total bubbles included:', len(all_data))

if get_averaged:
    for cind, cth in enumerate(crit_radList):
        for tind, tsh in enumerate(crit_threshList):
            path = average_file(*exp_params)+'_critrad'+str(cth)+'_crittsh'+str(tsh)+'.npy'
            if os.path.exists(path): continue

            stacks  = stack_bubbles(all_data, win, phieq, tsh, cth, plots)
            avstack = average_stacks(stacks, win, normal, plots=False)
            #except:
            #    print('Skipped cind, tind', cind, tind)
            #    continue
            np.save(path, avstack)
            print('Done cind, tind', cind, tind)

Looking at at T, m2, sigma: 0.2 0.7 0.3415271460137985
[ 5 10 15 20 25 30 35 40 45 50]
[1.171 1.342 1.513 1.683 1.854 2.025 2.196 2.366 2.537 2.708]
simList2Do [   0    1    2 ... 1997 1998 1999]

/cm/shared/apps/python/python37/lib/python3.7/site-packages/ipykernel_launcher.py:39: VisibleDeprecationWarning: Creating an ndarray from ragged nested sequences (which is a list-or-tuple of lists-or-tuples-or ndarrays with different lengths or shapes) is deprecated. If you meant to do this, you must specify 'dtype=object' when creating the ndarray.

Total bubbles included: 301
In [5]:
print('Looking at at T, m2, sigma:', temp, m2, sigmafld)
get_plotcomp = True

delt1 = 100

if get_plotcomp:
    varmat  = np.zeros((len(crit_radList), len(crit_threshList)))
    for cind, cth in enumerate(crit_radList):
        for tind, tsh in enumerate(crit_threshList):
            bubble = np.load(average_file(*exp_params)+'_critrad'+str(cth)+'_crittsh'+str(tsh)+'.npy')
            bubble[bubble==0.] = 'nan'

            bubble2measure = np.abs(bubble[0,0])
            nT, nN = np.shape(bubble2measure)
#             for crit_rad in crit_radList:
#                 for crit_thresh in crit_threshList:
#                     tcen, xcen = find_nucleation_center(bubble2measure, phieq, crit_thresh, crit_rad)
#                     tcen -= 35
#                     tl,tr = max(0, tcen-delt1), min(nT, tcen+delt1+1)
#                     xl,xr = max(0, xcen-delt1), min(nN, xcen+delt1+1)

#                     err_f = bubble[1, 0][tl:tr,xl:xr]  #field variance
#                     err_m = bubble[1, 1][tl:tr,xl:xr]  #momentum variance
#                     varmat[cind, tind] += np.nanmean(np.abs(err_f))
            tcen, xcen = find_nucleation_center(bubble2measure, phieq, tsh, cth)
            tcen -= 35
            tl,tr = max(0, tcen-delt1), min(nT, tcen+delt1+1)
            xl,xr = max(0, xcen-delt1), min(nN, xcen+delt1+1)

            err_f = bubble[1, 0][tl:tr,xl:xr]  #field variance
            err_m = bubble[1, 1][tl:tr,xl:xr]  #momentum variance
            varmat[cind, tind] += np.nanmean(np.abs(err_f))

    colmin, rowmin = np.where(varmat == np.nanmin(varmat))
    final_crit_rad = crit_radList[colmin][0]
    final_crit_thresh = crit_threshList[rowmin][0]

    fig, ax = plt.subplots(1,1, figsize = (3.5,3))
    ext = [crit_threshList[0], crit_threshList[-1], crit_radList[0], crit_radList[-1]]

    im0 = plt.imshow(varmat, interpolation=None, norm=LogNorm(), extent=ext, aspect='auto', origin='lower', cmap='RdBu')
    clb = plt.colorbar(im0)
    clb.ax.set_title(r'${\rm var} \, \left< \bar{\varphi} \right> $', size=11, horizontalalignment='center', verticalalignment='bottom')
    clb.ax.yaxis.set_offset_position('right')                         
    plt.plot(final_crit_thresh, final_crit_rad, color='white', marker='*')
    #plt.plot(final_crit_thresh + 0.5*(crit_threshList[1]-crit_threshList[0]), final_crit_rad + 0.5*(crit_radList[1]-crit_radList[0]), color='white', marker='*')

    xx, yy = np.meshgrid(crit_threshList, crit_radList)
    ax.contour(xx,yy,np.abs(np.log(varmat)), levels=6, aspect='auto', interpolation='gaussian', extent=ext, origin='lower', colors='k', linewidths=0.5)

    ax.set_ylabel(r'$R$')
    ax.set_xlabel(r'$\bar{\phi}$')
    beautify(ax, times=-90)
    ax.legend(title=r'$T = {:.2f}$'.format(temp), labelspacing=0., frameon='true', facecolor='white', framealpha=0.85, edgecolor='k', borderpad=0.3)
    fig.tight_layout()
    plt.savefig('./plots/residual_averaging_T'+str(temp)+'.pdf')
    plt.show()

Looking at at T, m2, sigma: 0.2 0.7 0.3415271460137985

/cm/shared/apps/python/python37/lib/python3.7/site-packages/ipykernel_launcher.py:49: UserWarning: The following kwargs were not used by contour: 'aspect', 'interpolation'
No artists with labels found to put in legend.  Note that artists whose label start with an underscore are ignored when legend() is called with no argument.

No artists with labels found to put in legend.  Note that artists whose label start with an underscore are ignored when legend() is called with no argument.
No description has been provided for this image
In [6]:
if True:
    tp = 0 # 0 for average, 1 for error
    cp = 0 # 0 - field, 1 - momentum
    delt = 100; print(delt)

    fig, ax = plt.subplots(len(crit_radList), len(crit_threshList), figsize = (len(crit_threshList)*5, len(crit_radList)*5), sharey=True, sharex=True)
    for cind, cth in enumerate(crit_radList):
        for tind, tsh in enumerate(crit_threshList):
            bubble = np.load(average_file(*exp_params)+'_critrad'+str(cth)+'_crittsh'+str(tsh)+'.npy')
            bubble[bubble==0.] = 'nan'

            bubble2measure = np.abs(bubble[tp, cp])
            if cp==0:
                bubble2measure[bubble2measure > tsh] = tsh
            try:
                tcen, xcen = find_nucleation_center(bubble2measure, phieq, tsh, cth)
            except:
                continue
            tcen-=35
            nT, nN = np.shape(bubble2measure)
            tl,tr = max(0, tcen-delt), min(nT, tcen+delt+1)
            xl,xr = max(0, xcen-delt), min(nN, xcen+delt+1)
            ext = np.array([xl-xcen,xr-xcen,tl-tcen,tr-tcen])

            bubble2plot = bubble2measure[tl:tr,xl:xr]

            im0  = ax[cind,tind].imshow(bubble2plot, extent=ext, interpolation=None, origin='lower', cmap='tab20c')
            clb0 = plt.colorbar(im0, ax = ax[cind,tind], shrink=0.6)
            lab  = r'${{\rm size}}={:.0f}'.format(cth)+r'$, {{\rm thresh}}={:.2f}'.format(tsh)
            ax[cind,tind].plot(0,0, ls=None, marker='o', color='k', label=lab)
            beautify(ax[cind,tind])
            ax[cind,tind].legend(loc=4, fancybox=True, frameon=True, framealpha=0.75, borderpad=0.3)

            if tsh == final_crit_thresh and cth == final_crit_rad:
                ax[cind,tind].plot(0., 0., color='red', ms=10, marker='*')

    plt.tight_layout()
    plt.show()

100
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In [7]:
rfin = np.argwhere(crit_radList == final_crit_rad)[0,0]
tfin = np.argwhere(crit_threshList == final_crit_thresh)[0,0]
crit_rad = final_crit_rad
crit_thresh = final_crit_thresh
print(rfin, tfin, crit_rad, crit_thresh)

get_final_averaged = True
if get_final_averaged:
    path = decay_times_file(*exp_params, minSim, maxSim, nTimeMAX)
    if os.path.exists(path):
        decay_times = np.load(path)

        minDecTime = 256
        alltimes   = decay_times[:,1]
        simList2Do = decay_times[alltimes>=minDecTime, 0]

    all_data = []
    fig, ax = plt.subplots(1,1, figsize = (4.5,2.5))
    for sim in simList2Do:
        path2RESTsim = rest_sim_location(*exp_params, sim)
        if os.path.exists(path2RESTsim):
            sim, bubble, totbeta = np.load(path2RESTsim, allow_pickle=True)

            real = np.copy(bubble)
            real = np.abs(real[0])
            real = gaussian_filter(real, 1, mode='nearest')
            nT, nN = np.shape(real)
            tcen, xcen = find_nucleation_center(real, phieq, crit_thresh, crit_rad)
            t, x = np.linspace(-tcen, nT-1-tcen, nT), np.linspace(-xcen, nN-1-xcen, nN)

        #    test = bubble[0, tcen, xcen-50:xcen+50]
        #    if np.nanmean(test) < 0:
        #        bubble = - bubble

            test = bubble[0, tcen, xcen-100:xcen+100]
            plt.plot(test, lw=1)
            plt.title(sim)

            all_data.append(np.array([sim, bubble]))
    print('Total bubbles included:', len(all_data))
    plt.show()

    stacks  = stack_bubbles(all_data, win, phieq, crit_thresh, crit_rad, False)
    avstack = average_stacks(stacks, win, normal, True)
    np.save(average_file(*exp_params), avstack)

2 2 15 1.513

/cm/shared/apps/python/python37/lib/python3.7/site-packages/ipykernel_launcher.py:39: VisibleDeprecationWarning: Creating an ndarray from ragged nested sequences (which is a list-or-tuple of lists-or-tuples-or ndarrays with different lengths or shapes) is deprecated. If you meant to do this, you must specify 'dtype=object' when creating the ndarray.

Total bubbles included: 828
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719 simulations for this combination.

/home/dpirvu/project/paper_prefactor/bubble_codes/bubble_tools.py:556: RuntimeWarning: Mean of empty slice
  mean = np.nanmean(whole_bubble, axis=0)

/home/dpirvu/.local/lib/python3.7/site-packages/numpy/lib/nanfunctions.py:1671: RuntimeWarning: Degrees of freedom <= 0 for slice.
  keepdims=keepdims)

No artists with labels found to put in legend.  Note that artists whose label start with an underscore are ignored when legend() is called with no argument.

No artists with labels found to put in legend.  Note that artists whose label start with an underscore are ignored when legend() is called with no argument.

No artists with labels found to put in legend.  Note that artists whose label start with an underscore are ignored when legend() is called with no argument.

No artists with labels found to put in legend.  Note that artists whose label start with an underscore are ignored when legend() is called with no argument.
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In [8]:
def plot_zoomin(real, threshold=2., winsize=100, title=None):
    real = real[0]
    nT, nN = np.shape(real)
    t_centre, x_centre = find_nucleation_center(real, phieq, crit_thresh, crit_rad)
    tl_stop, tr_stop = int(max(0, t_centre - winsize)), int(min(nT, t_centre + winsize//2))
    xl_stop, xr_stop = int(max(0, x_centre - winsize)), int(min(nN, x_centre + winsize))
    real = real[tl_stop:tr_stop, xl_stop:xr_stop]
    nT, nN = np.shape(real)
    tcen, xcen = find_nucleation_center(real, phieq, crit_thresh, crit_rad)
    t, x = np.linspace(-tcen, nT-1-tcen, nT), np.linspace(-xcen, nN-1-xcen, nN)
    cds = np.abs(real) > threshold
    real[cds] = threshold
    simple_imshow([real], x, t, title=title, contour=False, ret=False)
    return
In [9]:
path = average_file(*exp_params)+'_critrad'+str(crit_rad)+'_crittsh'+str(crit_thresh)+'.npy'
average_bubble_filtered = np.load(path)

plot_zoomin(average_bubble_filtered[0], threshold=20, winsize=60)

average_bubble_loaded = np.load(average_file(*exp_params))

plot_zoomin(average_bubble_loaded[0], threshold=20, winsize=60)

No artists with labels found to put in legend.  Note that artists whose label start with an underscore are ignored when legend() is called with no argument.
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No artists with labels found to put in legend.  Note that artists whose label start with an underscore are ignored when legend() is called with no argument.
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In [10]:
titl = [r'$\bar{\phi}$',  r'$\bar{\Pi}$']

for avbub in [average_bubble_loaded, average_bubble_filtered]:
    bubble2measure = avbub[0][0]
    nT, nN = np.shape(bubble2measure)
    tcen, xcen = find_nucleation_center(bubble2measure, phieq, crit_thresh, crit_rad)
    print(tcen, xcen)
    
    tcen-= 40
    size = 130
    tl,tr = max(0, tcen-size), min(nT, tcen+size+1)
    xl,xr = max(0, xcen-size), min(nN, xcen+size+1)
    ext = np.array([xl-xcen,xr-xcen,tl-tcen,tr-tcen])

    fig, ax = plt.subplots(1, 2, figsize = (6, 3), sharey=True)
    for cp in range(2):
        # 0 - field, 1 - momentum
        bubble2plot = np.copy(avbub[0][cp,tl:tr,xl:xr])

        im = ax[cp].imshow(bubble2plot, interpolation=None, extent=ext, origin='lower', cmap='tab20b')
        cbar = plt.colorbar(im, ax=ax[cp], shrink=0.7)
        cbar.ax.set_title(titl[cp])

        nT, nN = np.shape(bubble2plot)
        tt = np.linspace(tl-tcen, tr-tcen, nT)
        xx = np.linspace(xl-xcen, xr-xcen, nN)
        ttt1, xxx1 = np.meshgrid(tt, xx)

        lavs = [8, 6][cp]
    #  ax[cp].contour(xxx1, ttt1, bubble2plot.T, levels=lavs, aspect='auto', interpolation=None, extent=ext, origin='lower', colors='k',linewidths=0.5)
        ax[cp].set_xlabel(r'$r$')
    ax[0].set_ylabel(r'$t$')
    beautify(ax, times=-90)
    plt.tight_layout()
    plt.savefig('./plots/average_bubble'+str(temp)+'.pdf')
    plt.show()

No artists with labels found to put in legend.  Note that artists whose label start with an underscore are ignored when legend() is called with no argument.

No artists with labels found to put in legend.  Note that artists whose label start with an underscore are ignored when legend() is called with no argument.

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No artists with labels found to put in legend.  Note that artists whose label start with an underscore are ignored when legend() is called with no argument.

No artists with labels found to put in legend.  Note that artists whose label start with an underscore are ignored when legend() is called with no argument.

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In [11]:
tp = 0# 0 for average, 1 for error

titl = [r'$\bar{\phi}$',  r'$\bar{\Pi}$']

for avbub in [average_bubble_loaded, average_bubble_filtered]:
    bubble2measure = avbub[0][0]
    nT, nN = np.shape(bubble2measure)
    tcen, xcen = find_nucleation_center(bubble2measure, phieq, crit_thresh, crit_rad)
    print(tcen, xcen)
    size = 100
    tl,tr = max(0, tcen-size), min(nT, tcen+size+1)
    xl,xr = max(0, xcen-size), min(nN, xcen+size+1)
    ext = np.array([xl-xcen,xr-xcen,tl-tcen,tr-tcen])

    fld_fld = np.copy(avbub[0][0])
    fld_mom = np.copy(avbub[0][1])

    fld_fld[fld_fld > crit_thresh] = crit_thresh
    fld_fld = gaussian_filter1d(fld_fld, 1, mode='nearest')
    fld_mom = gaussian_filter1d(fld_mom, 1, mode='nearest')

    fld_fld = fld_fld[tl:tr, xl:xr]
    fld_mom = fld_mom[tl:tr, xl:xr]

    kinetic   = 0.5*fld_mom**2.
    gradient  = 0.5*np.gradient(fld_fld, dx)[1]**2.
    potential = V(fld_fld)

    total = kinetic + gradient + potential
    picks = [kinetic, gradient, potential, total]
    titl = [r'$\rm Kinetic$', r'$\rm Gradient$', r'$\rm Potential$', r'$\rm Total$']

    fig, ax = plt.subplots(1, 4, figsize = (13, 3), sharey=True)
    for fi, field in enumerate(picks):

        im = ax[fi].imshow(field, extent=ext, origin='lower', cmap='tab20c')
        cbar = fig.colorbar(im, ax=ax[fi], shrink=0.7)

        nT, nN = np.shape(field)
        tt = np.linspace(tl-tcen, tr-tcen, nT)
        xx = np.linspace(xl-xcen, xr-xcen, nN)
        ttt1, xxx1 = np.meshgrid(tt, xx)
        ax[fi].contour(xxx1, ttt1, field.T, levels=8, aspect='auto', interpolation='none', extent=ext, origin='lower', colors='k', linewidths=0.5)

        ax[fi].set_title(titl[fi])
        ax[fi].set_xlabel(r'$r$')
    ax[0].set_ylabel(r'$t$')
    plt.savefig('./plots/energies.pdf')
    plt.show()

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/cm/shared/apps/python/python37/lib/python3.7/site-packages/ipykernel_launcher.py:43: UserWarning: The following kwargs were not used by contour: 'aspect', 'interpolation'
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In [ ]:
In [ ]: